Parts or all of the course will be given via video conferencing software.

To receive updates on the organizational details, please register for the course in Moodle by April 20!

Kommentar

Course Content Description

Seminar participants will explore current research trends and established approaches in the Data Science field.

Participants can choose to complete either the theoretical or the practical research project track as part of this seminar.

For the theoretical research project, participants will pick a topic according to their own interests, or from a pool of suggestions that will be provided.

For their topic, the participants will give an overview of the state-of-the-art relevant to that topicin a presentation during the seminar (30 min) and a term paper (8 - 10 pages per person, ACM style) due at the end of the seminar.

Through this process, which the lecturers supervise and guide, the participants will train their ability to:

find, organize, and systematically read relevant research papers;

analyze, compare, and contrast research approaches and findings;

structure, write, and format an academic paper;

present their work using appropriate presentation techniques and presentation aids;

answer questions and discuss their work with peers.

The theoretical research project is best suited to compile a state-of-the-art review in preparation for a subsequent bachelor's or master's thesis in the same area.

For the practical research project, participants will implement a system that solves an applied real-world problem. Participants can suggest a problem or choose from suggestions that will be provided. In addition to delivering a functioning application, completing this seminar requires giving a presentation (30 min) about the project and compiling a developer documentation for the application (min 3 pages ACM style per person).

By completing the practical research track, participants will gain hands-experience with state-of-the-art methods and technologies and train their application development skills.

Topic suggestions for both tracks include, but are not limited to:

Recommender Systems

Literature Recommendation

Collaborator Recommendation

Plagiarism Detection

Semantic Text Analysis

Analysis of Nontextual Content Features

Machine Learning Approaches

Mathematical Content Analysis

Blockchain Applications

Open Science

Trusted Timestamping

Confidential Information Retrieval

News Analysis

Semantic Analysis of News Articles

Dissemination of Information

News Framing Analysis

Clustering Related News

Artificial Neural Networks for Industrial Applications

Time Series Analysis for Soft Sensors

Time Series Forecasting for Predictive Quality Control

Image Recognition for Waste Product Classification

Transfer Learning for Simulation and Real-World Data

Explainability of Decision Processes in Artificial Neural Networks

Object Recognition in Convolutional Neural Networks

Visualization of Network Activity

Structure of Learning Representations

Importance of Network Areas for the Learning Task

By successfully completing the seminar, participants will achieve valuable preparation in terms of the knowledgeand the methodological skills required to successfully complete a bachelor’s or master’s thesis in the groups of Prof. Meisen and Prof. Gipp.